Basketball action recognition based on the combination of YOLO and a deep fuzzy LSTM network

Khobdeh, Soroush Babaee; Yamaghani, Mohammad Reza; Sareshkeh, Siavash Khodaparast

doi:10.1007/s11227-023-05611-7

Cited by 7 publications

(2 citation statements)

References 66 publications

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“…With the impressive potential of deep neural networks and the guidance of manual annotation, many practical applications [14][15][16] have sprung up. Among them, pedestrian re-identification has received much attention because it is concerned with surveillance and security in some application scenarios.…”

Section: Related Work 21 Pedestrian Re-identificationmentioning

confidence: 99%

FCL: Pedestrian Re-Identification Algorithm Based on Feature Fusion Contrastive Learning

Li,

Zhang,

Gao

et al. 2024

Electronics

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Pedestrian re-identification leverages computer vision technology to achieve cross-camera matching of pedestrians; it has recently led to significant progress and presents numerous practical applications. However, current algorithms face the following challenges: (1) most of the methods are supervised, heavily relying on specific datasets, and lacking robust generalization capabilities; (2) it is hard to extract features because the elongated and narrow shape of pedestrian images introduces uneven feature distributions; (3) the substantial imbalance between positive and negative samples. To address these challenges, we introduce a novel pedestrian re-identification unsupervised algorithm called Feature Fusion Contrastive Learning (FCL) to extract more effective features. Specifically, we employ circular pooling to merge network features across different levels for pedestrian re-identification to improve robust generalization capability. Furthermore, we propose a feature fusion pooling method, which facilitates a more efficient distribution of feature representations across pedestrian images. Finally, we introduce FocalLoss to compute the clustering-level loss, mitigating the imbalance between positive and negative samples. Through extensive experiments conducted on three prominent datasets, our proposed method demonstrates promising performance, with an average 3.8% improvement in FCL’s mAP indicators compared to baseline results.

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Section: Related Work 21 Pedestrian Re-identificationmentioning

confidence: 99%

FCL: Pedestrian Re-Identification Algorithm Based on Feature Fusion Contrastive Learning

Li,

Zhang,

Gao

et al. 2024

Electronics

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“…In recent years, with the rapid development of object detection technology based on deep learning, this technology has been widely used in various fields such as action recognition [7], traffic signs detection [8], and industrial defect detection [9][10][11][12]. Its application in waste classification has also been actively explored by scholars to achieve intelligent and automated waste identification [13][14][15], ensuring more accurate and efficient classification.…”

Section: Introductionmentioning

confidence: 99%

LWCNet: A Lightweight and Efficient Algorithm for Household Waste Detection and Classification Based on Deep Learning

Wang,

Sun

et al. 2024

Preprint

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The existing waste classification algorithms based on computer vision are hindered by issues such as low classification accuracy, a large number of model parameters, and high computational complexity. Aiming at these problems, a Lightweight Waste Classification Network (LWCNet) based on the basic framework of YOLOv8n is designed and proposed in this paper. Firstly, a detection head based on the self-attention mechanism, SAHead, is proposed to capture a wider range of contextual information. Secondly, the lightweight convolution GSConv is employed to optimize the standard convolution in YOLOv8n. Combined with GELAN, the GRCSPELAN module is designed to reduce the model size without compromising performance. Finally, the AIFI module is introduced to enhance the intra-scale feature interaction ability. The experimental results indicate that LWCNet effectively reduces the model size and enhances detection efficiency, providing reliable theoretical support for the automation of household waste detection and classification.

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A survey of video-based human action recognition in team sports

Yin,

Sinnott,

Jayaputera

2024

Artif Intell Rev

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Over the past few decades, numerous studies have focused on identifying and recognizing human actions using machine learning and computer vision techniques. Video-based human action recognition (HAR) aims to detect actions from video sequences automatically. This can cover simple gestures to complex actions involving multiple people interacting with objects. Actions in team sports exhibit a different nature compared to other sports, since they tend to occur at a faster pace and involve more human-human interactions. As a result, research has typically not focused on the challenges of HAR in team sports. This paper comprehensively summarises HAR-related research and applications with specific focus on team sports such as football (soccer), basketball and Australian rules football. Key datasets used for HAR-related team sports research are explored. Finally, common challenges and future work are discussed, and possible research directions identified.

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Basketball action recognition based on the combination of YOLO and a deep fuzzy LSTM network

Cited by 7 publications

References 66 publications

FCL: Pedestrian Re-Identification Algorithm Based on Feature Fusion Contrastive Learning

FCL: Pedestrian Re-Identification Algorithm Based on Feature Fusion Contrastive Learning

LWCNet: A Lightweight and Efficient Algorithm for Household Waste Detection and Classification Based on Deep Learning

A survey of video-based human action recognition in team sports

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